#librerias necesarias


#library (Biostrings) # Manipulacion de secuencias y alineamientos

library(ggplot2)
library(plotly)
library (RColorBrewer)
library(caret)
library(tidyr)
library(dplyr)
#library(reshape2)
library(psych)
library(irlba)
library(fpc)

Data

#librerias necesarias
#library (Biostrings) # Manipulacion de secuencias y alineamientos
library(ggplot2)
library(plotly)
library (RColorBrewer)
library(caret)
library(tidyr)
library(dplyr)
#library(reshape2)
library(psych)
library(irlba)
library(fpc)

NSFW

train_v1<-read.csv("ccs.new_train_v1.csv", stringsAsFactors = FALSE)%>%select(-id)
trainCat<-train_v1%>%select(type_cat, image_name)
test_v1<-read.csv("ccs.new_test_v1.csv" , stringsAsFactors = FALSE)%>%select(-id)

Distance

NSFWtrain<-read.csv("nsfwOut_train.csv" , stringsAsFactors = FALSE, header=FALSE)%>%
                rename(image_name=V1, nsfw_val=V2)%>%
                    mutate(nsfw_val_log=log(nsfw_val))
NSFWtest<-read.csv("nsfwOut_test.csv" , stringsAsFactors = FALSE, header=FALSE)%>%
  
                rename(image_name=V1, nsfw_val=V2)%>%
                    mutate(nsfw_val_log=log(nsfw_val))
NSFWtrain<-left_join(trainCat,NSFWtrain, by=c("image_name"="image_name"))
NSFWtrain%>%
  spread(type_cat, nsfw_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~Type_1, name="Type_1") %>%
          add_histogram(x = ~Type_2,name="Type_2" ) %>%
           add_histogram(x = ~Type_3, name="Type_3" ) %>%
        layout(barmode = "overlay",title = "NSFW value")

## Logaritmic
NSFWtrain%>%
  spread(type_cat, nsfw_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(Type_1), name="Type_1") %>%
          add_histogram(x = ~log(Type_2),name="Type_2" ) %>%
           add_histogram(x = ~log(Type_3), name="Type_3" ) %>%
        layout(barmode = "overlay", title = "log(NSFW value)")

##Check consistency at both sets
head(cbind(train.mean = lapply(NSFWtrain%>%select(nsfw_val,nsfw_val_log),
                               function(x){mean(x,na.rm=TRUE)} ),
              train.sd = lapply(NSFWtrain%>%select(nsfw_val,nsfw_val_log),
                            function(x){sd(x,na.rm=TRUE)} ),
                 test.mean = lapply(NSFWtest%>%select(nsfw_val,nsfw_val_log),
                                   function(x){mean(x,na.rm=TRUE)}),
                      test.sd = lapply(NSFWtest%>%select(nsfw_val,nsfw_val_log),
                                       function(x){sd(x,na.rm=TRUE)})))
             train.mean train.sd test.mean test.sd  
nsfw_val     0.2106212  0.25315  0.2378719 0.2669555
nsfw_val_log -2.463487  1.577257 -2.298662 1.570717 

hashing

Distrain<-read.csv("train_distance.csv" , stringsAsFactors = FALSE,
                   header=FALSE)%>%
                     rename(image_name=V1, dist_val=V2)%>%
                        mutate(dist_val_log=log(dist_val))
  
Distest<-read.csv("test_distance.csv" , stringsAsFactors = FALSE,
                  header=FALSE)%>%
                      rename(image_name=V1, dist_val=V2)%>%
                          mutate(dist_val_log=log(dist_val))
Distrain<-left_join(trainCat,Distrain, by=c("image_name"="image_name"))
##Check consistency at both sets
head(cbind(train.mean = lapply(Distrain%>%select(dist_val,dist_val_log),
                               function(x){mean(x,na.rm=TRUE)} ),
              train.sd = lapply(Distrain%>%select(dist_val,dist_val_log),
                            function(x){sd(x,na.rm=TRUE)} ),
                 test.mean = lapply(Distest%>%select(dist_val,dist_val_log),
                                   function(x){mean(x,na.rm=TRUE)}),
                      test.sd = lapply(Distest%>%select(dist_val,dist_val_log),
                                       function(x){sd(x,na.rm=TRUE)})))
             train.mean train.sd  test.mean test.sd  
dist_val     6449660    1872454   6249978   1914739  
dist_val_log 15.61894   0.3911204 15.57813  0.4301607
#scale train
Distrain_s<- scale(as.matrix(Distrain%>%select(dist_val, dist_val_log)),
                   center=TRUE,scale=TRUE)
#scale testing
Distest_s<- scale(as.matrix(Distest%>%select(dist_val, dist_val_log),
                                attr(Distrain_s, "scaled:center"),
                                    attr(Distrain_s, "scaled:scale") ) )
##Transform both sets
Distrain<-cbind(Distrain%>%select(type_cat, image_name) , Distrain_s[,])
Distest<-cbind(Distest%>%select( image_name) , Distest_s[,])
Distrain%>%
  spread(type_cat, dist_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~Type_1, name="Type_1") %>%
          add_histogram(x = ~Type_2,name="Type_2" ) %>%
           add_histogram(x = ~Type_3, name="Type_3" ) %>%
        layout(barmode = "overlay",
               title = "Distance")

### Logaritm
Distrain%>%
  spread(type_cat, dist_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(Type_1), name="Type_1") %>%
          add_histogram(x = ~log(Type_2),name="Type_2" ) %>%
           add_histogram(x = ~log(Type_3), name="Type_3" ) %>%
        layout(barmode = "overlay",
               title = "Log (Distance)")

## Check
Distrain[1:5,1:4]
Distest[1:5,1:3]
##Check consistency at both sets
head(cbind(train.mean = lapply(Distrain%>%select(dist_val,dist_val_log),
                               function(x){mean(x,na.rm=TRUE)} ),
              train.sd = lapply(Distrain%>%select(dist_val,dist_val_log),
                            function(x){sd(x,na.rm=TRUE)} ),
                 test.mean = lapply(Distest%>%select(dist_val,dist_val_log),
                                   function(x){mean(x,na.rm=TRUE)}),
                      test.sd = lapply(Distest%>%select(dist_val,dist_val_log),
                                       function(x){sd(x,na.rm=TRUE)})))
             train.mean    train.sd test.mean     test.sd
dist_val     -8.890245e-17 1        -2.942804e-18 1      
dist_val_log -2.192649e-15 1        4.123298e-16  1      

Networks

pHashtrain<-read.csv("hashes_16_phash_training.csv" , stringsAsFactors = FALSE, header=FALSE)%>%select(-V2)%>%
          #mutate_each(funs(whatever = ./15), V3:V66)
            mutate_each(funs(./15), V3:V66)
pHashtest<-read.csv("hashes_16_phash_testing.csv" , stringsAsFactors = FALSE, header=FALSE)%>%select(-V2)%>%
          mutate_each(funs(./15), V3:V66)
### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(pHashtrain)
nzv_12<- nearZeroVar(pHashtest)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        pHashtrain<- pHashtrain[, -unique(c(nzv_11,nzv_12))]
        pHashtest<- pHashtest[, -unique(c(nzv_11,nzv_12))]
        }
##Check consistency at both sets
head(cbind(train.mean = lapply(pHashtrain[,-1], mean),
          train.sd = lapply(pHashtrain[,-1], sd),
                test.mean = lapply(pHashtest[,-1], mean),
                      test.sd = lapply(pHashtest[,-1], sd)))
   train.mean train.sd  test.mean test.sd  
V3 0.4498535  0.3162274 0.4458333 0.3241125
V4 0.4669371  0.2922544 0.4648438 0.2949693
V5 0.4714447  0.3191498 0.4415365 0.3182941
V6 0.4573811  0.3190504 0.4592448 0.3193908
V7 0.5020509  0.3150338 0.4847656 0.3218135
V8 0.4611675  0.285993  0.4653646 0.2834234
# conduct PCA on training dataset
num.cols <- names(which(sapply( pHashtrain, function(x) is.numeric(x) )))
pca <- prcomp(pHashtrain[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)
### Plot importance
p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~pixel features ")
ggplotly(p)

#Replace features by PCA components
pHashtrain_pca<-as.data.frame(pca$x[,1:40])
colnames(pHashtrain_pca)<-paste("pca_pHash_",1:40,sep="")
### APPLY TO TESTING DATA 
pHashtest_pca<-as.data.frame(predict(pca, newdata=pHashtest[,num.cols])[,1:40])
colnames(pHashtest_pca)<-paste("pca_pHash_",1:40,sep="")
pHashtrain<-cbind(pHashtrain[,1],pHashtrain_pca)
pHashtest<-cbind(pHashtest[,1],pHashtest_pca)
colnames(pHashtrain)[1]<-colnames(pHashtest)[1]<-"image_name"
pHashtrain<-left_join(trainCat,pHashtrain, by=c("image_name"="image_name"))
#### Plot Four first dimensions
p <- plot_ly(pHashtrain, x = ~pca_pHash_1, y = ~pca_pHash_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))
p

pHashtrain[1:5,1:5]
pHashtest[1:5,1:5]

InceptionV3 Net

Namestrain<-read.csv("Names_train_features.csv" , stringsAsFactors = FALSE)
Namestest<-read.csv("Names_test_features.csv" , stringsAsFactors = FALSE)
##### Inceptionv3
Inceptrain<- cbind(Namestrain,read.csv("Inception3_train_features.csv", stringsAsFactors = FALSE) )
Inceptest<- cbind(Namestest, read.csv("Inception3_test_features.csv", stringsAsFactors = FALSE) )
##### ResNet50
RN50train<-cbind(Namestrain, read.csv("ResNet50_train_features.csv", stringsAsFactors = FALSE) )
RN50test<-cbind(Namestest ,read.csv("ResNet50_test_features.csv", stringsAsFactors = FALSE) )
#####VGG19 
VGG19train<-cbind(Namestrain ,read.csv("VGG19_train_features.csv" , stringsAsFactors = FALSE) )
VGG19test<-cbind(Namestest, read.csv("VGG19_test_features.csv" , stringsAsFactors = FALSE) )

ResNet50 Net

### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(Inceptrain)
nzv_12<- nearZeroVar(Inceptest)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        Inceptrain<- Inceptrain[, -unique(c(nzv_11,nzv_12))]
        Inceptrain<- Inceptest[, -unique(c(nzv_11,nzv_12))]
        }
##Check consistency at both sets
head(cbind(train.mean = lapply(Inceptrain[,-1], mean),
          train.sd = lapply(Inceptrain[,-1], sd),
                test.mean = lapply(Inceptest[,-1], mean),
                      test.sd = lapply(Inceptest[,-1], sd)))
   train.mean   train.sd     test.mean    test.sd    
X0 0.001380052  0.01141086   0.0009170967 0.002802622
X1 0.003639353  0.0210514    0.00374452   0.02601869 
X2 0.0005113326 0.001457968  0.0004813768 0.001247876
X3 0.0005140099 0.001690451  0.0004951666 0.002232964
X4 0.0003072042 0.0007335646 0.000539596  0.005894615
X5 0.001228221  0.0157454    0.001042944  0.01056041 
#### Plot first
Inceptrain%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~X0, name="X0") %>%
          add_histogram(x = ~X1,name="X1" ) %>%
           add_histogram(x = ~X2, name="X2" ) %>%
              add_histogram(x = ~X3, name="X3" ) %>%
                add_histogram(x = ~X4, name="X4" ) %>%
                    add_histogram(x = ~X5, name="X5" ) %>%
                      add_histogram(x = ~X6, name="X6" ) %>%
                           add_histogram(x = ~X7, name="X7" ) %>%
                                add_histogram(x = ~X8, name="X8" ) %>%
                                     add_histogram(x = ~X9, name="X9" ) %>%
                                        add_histogram(x = ~X10, name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "Inceptrain_outputs")

#### Plot log
Inceptrain%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(X0), name="X0") %>%
          add_histogram(x = ~log(X1),name="X1" ) %>%
           add_histogram(x = ~log(X2), name="X2" ) %>%
              add_histogram(x = ~log(X3), name="X3" ) %>%
                add_histogram(x = ~log(X4), name="X4" ) %>%
                    add_histogram(x = ~log(X5), name="X5" ) %>%
                      add_histogram(x = ~log(X6), name="X6" ) %>%
                           add_histogram(x = ~log(X7), name="X7" ) %>%
                                add_histogram(x = ~log(X8), name="X8" ) %>%
                                     add_histogram(x = ~log(X9), name="X9" ) %>%
                                        add_histogram(x = ~log(X10), name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "log(Inceptrain_outputs)")

####################################################################################################
####################################################################################################
# conduct PCA on training dataset
num.cols <- names(which(sapply( Inceptrain, function(x) is.numeric(x) )))
pca <- prcomp(Inceptrain[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)
### Plot importance
p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~Inceptrain features ")
ggplotly(p)

####################################################################################################
####################################################################################################
####################################################################################################
#Replace features by PCA components
Inceptrain_pca<-as.data.frame(pca$x[,1:50])
colnames(Inceptrain_pca)<-paste("pca_Incep3_",1:50,sep="")
### APPLY TO TESTING DATA 
Inceptest_pca<-as.data.frame(predict(pca, newdata=Inceptest[,num.cols])[,1:50])
colnames(Inceptest_pca)<-paste("pca_Incep3_",1:50,sep="")
Inceptrain<-cbind(Inceptrain[,1],Inceptrain_pca)
Inceptest<-cbind(Inceptest[,1],Inceptest_pca)
colnames(Inceptrain)[1]<-colnames(Inceptest)[1]<-"image_name"
Inceptrain<-left_join(trainCat,Inceptrain, by=c("image_name"="image_name"))
#### Plot Four first dimensions
p <- plot_ly(Inceptrain, x = ~pca_Incep3_1, y = ~pca_Incep3_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))
p

#### Plot first
Inceptrain%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~pca_Incep3_1, name="cmp1") %>%
          add_histogram(x = ~pca_Incep3_2,name="cmp2" ) %>%
           add_histogram(x = ~pca_Incep3_3, name="cmp3" ) %>%
              add_histogram(x = ~pca_Incep3_4, name="cmp4" ) %>%
                add_histogram(x = ~pca_Incep3_5, name="cmp5" ) %>%
                    add_histogram(x = ~pca_Incep3_6, name="cmp6" ) %>%
                      add_histogram(x = ~pca_Incep3_7, name="cmp7" ) %>%
                           add_histogram(x = ~pca_Incep3_8, name="cmp8" ) %>%
                                add_histogram(x = ~pca_Incep3_9, name="cmp9" ) %>%
                                     add_histogram(x = ~pca_Incep3_10, name="cmp10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "Incep3_outputs")

Inceptrain[1:5,1:5]
Inceptest[1:5,1:5]

VGG19 Net

### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(RN50train)
nzv_12<- nearZeroVar(RN50test)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        RN50train<- RN50train[, -unique(c(nzv_11,nzv_12))]
        RN50train<- RN50test[, -unique(c(nzv_11,nzv_12))]
        }
##Check consistency at both sets
head(cbind(train.mean = lapply(RN50train[,-1], mean),
          train.sd = lapply(RN50train[,-1], sd),
                test.mean = lapply(RN50test[,-1], mean),
                      test.sd = lapply(RN50test[,-1], sd)))
   train.mean   train.sd    test.mean    test.sd     
X0 0.0001554248 0.001995509 6.449305e-05 0.0002435841
X1 0.0003164812 0.001406066 0.0002199558 0.0004748997
X2 0.01424675   0.07969592  0.01391735   0.08040511  
X3 0.004219607  0.0270677   0.002187854  0.01284288  
X4 0.0001869516 0.002081979 0.0002245818 0.002877667 
X5 0.001481098  0.005702296 0.002190592  0.01193404  
#### Plot first
RN50train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~X0, name="X0") %>%
          add_histogram(x = ~X1,name="X1" ) %>%
           add_histogram(x = ~X2, name="X2" ) %>%
              add_histogram(x = ~X3, name="X3" ) %>%
                add_histogram(x = ~X4, name="X4" ) %>%
                    add_histogram(x = ~X5, name="X5" ) %>%
                      add_histogram(x = ~X6, name="X6" ) %>%
                           add_histogram(x = ~X7, name="X7" ) %>%
                                add_histogram(x = ~X8, name="X8" ) %>%
                                     add_histogram(x = ~X9, name="X9" ) %>%
                                        add_histogram(x = ~X10, name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "RN50train_outputs")

#### Plot log
RN50train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(X0), name="X0") %>%
          add_histogram(x = ~log(X1),name="X1" ) %>%
           add_histogram(x = ~log(X2), name="X2" ) %>%
              add_histogram(x = ~log(X3), name="X3" ) %>%
                add_histogram(x = ~log(X4), name="X4" ) %>%
                    add_histogram(x = ~log(X5), name="X5" ) %>%
                      add_histogram(x = ~log(X6), name="X6" ) %>%
                           add_histogram(x = ~log(X7), name="X7" ) %>%
                                add_histogram(x = ~log(X8), name="X8" ) %>%
                                     add_histogram(x = ~log(X9), name="X9" ) %>%
                                        add_histogram(x = ~log(X10), name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "log(RN50train_outputs)")

####################################################################################################
####################################################################################################
# conduct PCA on training dataset
num.cols <- names(which(sapply( RN50train, function(x) is.numeric(x) )))
pca <- prcomp(RN50train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)
### Plot importance
p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~RN50train features ")
ggplotly(p)

####################################################################################################
####################################################################################################
####################################################################################################
#Replace features by PCA components
RN50train_pca<-as.data.frame(pca$x[,1:40])
colnames(RN50train_pca)<-paste("pca_RN50_",1:40,sep="")
### APPLY TO TESTING DATA 
RN50test_pca<-as.data.frame(predict(pca, newdata=RN50test[,num.cols])[,1:40])
colnames(RN50test_pca)<-paste("pca_RN50_",1:40,sep="")
RN50train<-cbind(RN50train[,1],RN50train_pca)
RN50test<-cbind(RN50test[,1],RN50test_pca)
colnames(RN50train)[1]<-colnames(RN50test)[1]<-"image_name"
RN50train<-left_join(trainCat,RN50train, by=c("image_name"="image_name"))
#### Plot Four first dimensions
p <- plot_ly(RN50train, x = ~pca_RN50_1, y = ~pca_RN50_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))
p

#### Plot first
RN50train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~pca_RN50_1, name="cmp1") %>%
          add_histogram(x = ~pca_RN50_2,name="cmp2" ) %>%
           add_histogram(x = ~pca_RN50_3, name="cmp3" ) %>%
              add_histogram(x = ~pca_RN50_4, name="cmp4" ) %>%
                add_histogram(x = ~pca_RN50_5, name="cmp5" ) %>%
                    add_histogram(x = ~pca_RN50_6, name="cmp6" ) %>%
                      add_histogram(x = ~pca_RN50_7, name="cmp7" ) %>%
                           add_histogram(x = ~pca_RN50_8, name="cmp8" ) %>%
                                add_histogram(x = ~pca_RN50_9, name="cmp9" ) %>%
                                     add_histogram(x = ~pca_RN50_10, name="cmp10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "RN50_outputs")

RN50train[1:5,1:5]
RN50test[1:5,1:5]

Generate outputs

### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(VGG19train)
nzv_12<- nearZeroVar(VGG19test)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        VGG19train<- VGG19train[, -unique(c(nzv_11,nzv_12))]
        VGG19train<- VGG19test[, -unique(c(nzv_11,nzv_12))]
        }
##Check consistency at both sets
head(cbind(train.mean = lapply(VGG19train[,-1], mean),
          train.sd = lapply(VGG19train[,-1], sd),
                test.mean = lapply(VGG19test[,-1], mean),
                      test.sd = lapply(VGG19test[,-1], sd)))
   train.mean   train.sd     test.mean    test.sd     
X0 0.000152056  0.0009286555 0.0001250537 0.0005295175
X1 0.002372518  0.007802148  0.003164084  0.01971411  
X2 0.001756746  0.008479258  0.001684431  0.006802914 
X3 0.002490829  0.01408015   0.001586751  0.006244418 
X4 0.0007154238 0.002576961  0.0004852541 0.0009564264
X5 0.0007731305 0.002517532  0.0007732334 0.002002366 
#### Plot first
VGG19train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~X0, name="X0") %>%
          add_histogram(x = ~X1,name="X1" ) %>%
           add_histogram(x = ~X2, name="X2" ) %>%
              add_histogram(x = ~X3, name="X3" ) %>%
                add_histogram(x = ~X4, name="X4" ) %>%
                    add_histogram(x = ~X5, name="X5" ) %>%
                      add_histogram(x = ~X6, name="X6" ) %>%
                           add_histogram(x = ~X7, name="X7" ) %>%
                                add_histogram(x = ~X8, name="X8" ) %>%
                                     add_histogram(x = ~X9, name="X9" ) %>%
                                        add_histogram(x = ~X10, name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "VGG19train_outputs")

#### Plot log
VGG19train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(X0), name="X0") %>%
          add_histogram(x = ~log(X1),name="X1" ) %>%
           add_histogram(x = ~log(X2), name="X2" ) %>%
              add_histogram(x = ~log(X3), name="X3" ) %>%
                add_histogram(x = ~log(X4), name="X4" ) %>%
                    add_histogram(x = ~log(X5), name="X5" ) %>%
                      add_histogram(x = ~log(X6), name="X6" ) %>%
                           add_histogram(x = ~log(X7), name="X7" ) %>%
                                add_histogram(x = ~log(X8), name="X8" ) %>%
                                     add_histogram(x = ~log(X9), name="X9" ) %>%
                                        add_histogram(x = ~log(X10), name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "log(VGG19train_outputs)")

####################################################################################################
####################################################################################################
# conduct PCA on training dataset
num.cols <- names(which(sapply( VGG19train, function(x) is.numeric(x) )))
pca <- prcomp(VGG19train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)
### Plot importance
p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~VGG19train features ")
ggplotly(p)

####################################################################################################
####################################################################################################
####################################################################################################
#Replace features by PCA components
VGG19train_pca<-as.data.frame(pca$x[,1:40])
colnames(VGG19train_pca)<-paste("pca_VGG19_",1:40,sep="")
### APPLY TO TESTING DATA 
VGG19test_pca<-as.data.frame(predict(pca, newdata=VGG19test[,num.cols])[,1:40])
colnames(VGG19test_pca)<-paste("pca_VGG19_",1:40,sep="")
VGG19train<-cbind(VGG19train[,1],VGG19train_pca)
VGG19test<-cbind(VGG19test[,1],VGG19test_pca)
colnames(VGG19train)[1]<-colnames(VGG19test)[1]<-"image_name"
VGG19train<-left_join(trainCat,VGG19train, by=c("image_name"="image_name"))
#### Plot Four first dimensions
p <- plot_ly(VGG19train, x = ~pca_VGG19_1, y = ~pca_VGG19_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))
p

#### Plot first
VGG19train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~pca_VGG19_1, name="cmp1") %>%
          add_histogram(x = ~pca_VGG19_2,name="cmp2" ) %>%
           add_histogram(x = ~pca_VGG19_3, name="cmp3" ) %>%
              add_histogram(x = ~pca_VGG19_4, name="cmp4" ) %>%
                add_histogram(x = ~pca_VGG19_5, name="cmp5" ) %>%
                    add_histogram(x = ~pca_VGG19_6, name="cmp6" ) %>%
                      add_histogram(x = ~pca_VGG19_7, name="cmp7" ) %>%
                           add_histogram(x = ~pca_VGG19_8, name="cmp8" ) %>%
                                add_histogram(x = ~pca_VGG19_9, name="cmp9" ) %>%
                                     add_histogram(x = ~pca_VGG19_10, name="cmp10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "VGG19_outputs")

VGG19train[1:5,1:5]
VGG19test[1:5,1:5]

correlation ~rest features

write.csv(train_,"train_v1.csv",row.names = FALSE )
Error in is.data.frame(x) : object 'train_' not found


#scale train
feat_X3D_train_s<- scale(as.matrix(feat_X3D_train), center=TRUE,scale=TRUE)

#scale testing
feat_X3D_test_s<- scale(as.matrix(feat_X3D_test),
                         attr(feat_X3D_train_s, "scaled:center"),
                                        attr(feat_X3D_train_s, "scaled:scale") )


##Transform both sets
feat_X3D_train<-as.data.frame(feat_X3D_train_s[,])
feat_X3D_test<-as.data.frame(feat_X3D_test_s[,])

## Check
feat_X3D_train[1:5,1:5]
feat_X3D_test[1:5,1:5]
# 
# #to data frame
# feat_X3D_train_s<-as.data.frame(feat_X3D_train_s)
#     feat_X3D_test_s<-as.data.frame(feat_X3D_test_s)
# 
# 
# ##############################################3
# rep_X3D<-cbind(train.mean = lapply(feat_X3D_train_s[,-1], mean),
#                      train.sd = lapply(feat_X3D_train_s[,-1], sd),
#                            test.mean = lapply(feat_X3D_test_s[,-1], mean),
#                                 test.sd = lapply(feat_X3D_test_s[,-1], sd))
# head(rep_X3D)
# 
# 
# ##############################################3
# 
# 
# ### check in detail
# library(xda)
# xdatrain<-numSummary(feat_X3D_train_s)
# head(xdatrain)
# xdatest<-numSummary(feat_X3D_test_s)
# head(xdatest)
#xdatab2<-cha

#scale train
feat_pixel_train_s<- scale(as.matrix(feat_pixel_train), center=TRUE,scale=TRUE)

#scale testing
feat_pixel_test_s<- scale(as.matrix(feat_pixel_test),
                         attr(feat_pixel_train_s, "scaled:center"),
                                        attr(feat_pixel_train_s, "scaled:scale") )

##Transform both sets
feat_pixel_train<-as.data.frame(feat_pixel_train_s[,])
feat_pixel_test<-as.data.frame(feat_pixel_test_s[,])

## Check
feat_pixel_train[1:5,1:5]
feat_pixel_test[1:5,1:5]

# 
# 
# 
# #to data frame
# feat_pixel_train_s<-as.data.frame(feat_pixel_train_s)
#     feat_pixel_test_s<-as.data.frame(feat_pixel_test_s)
# 
# 
# ##############################################3
# rep_pixel<-cbind(train.mean = lapply(feat_pixel_train_s[,-1], mean),
#                      train.sd = lapply(feat_pixel_train_s[,-1], sd),
#                            test.mean = lapply(feat_pixel_test_s[,-1], mean),
#                                 test.sd = lapply(feat_pixel_test_s[,-1], sd))
# head(rep_pixel)
# 
# 
# ##############################################3
# 
# 
# ### check in detail
# library(xda)
# xdatrain<-numSummary(feat_pixel_train_s)
# head(xdatrain)
# xdatest<-numSummary(feat_pixel_test_s)
# head(xdatest)
# #xdatab2<-cha

correlation ~pixel features

####################################################################################################################
#Elegimos las componentes a utilizar
num.cols <- names(which(sapply(feat_pixel_train, function(x) is.numeric(x) )))

set.seed(1234)
num.cols<- sample(num.cols, 100)

svd_seq<-feat_pixel_train[,num.cols ]
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)

PCA from ~pixel features


# conduct PCA on training dataset
num.cols <- names(which(sapply(feat_pixel_train, function(x) is.numeric(x) )))

pca <- prcomp(feat_pixel_train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~pixel features ")
ggplotly(p)
#Replace features by PCA components
feat_pixel_train_pca<-as.data.frame(pca$x[,1:100])
colnames(feat_pixel_train_pca)<-paste("pca_pixel_",1:100,sep="")

### APPLY TO TESTING DATA 
feat_pixel_test_pca<-as.data.frame(predict(pca, newdata=feat_pixel_test[,num.cols])[,1:100])
colnames(feat_pixel_test_pca)<-paste("pca_pixel_",1:100,sep="")

scatter first 4 dimensions


library(plotly)

pc<-as.data.frame(pca$x[,1:4])%>%
                      rename(Dim1=PC1,
                             Dim2=PC2,
                             Dim3=PC3,
                             Dim4=PC4)

p <- plot_ly(pc, x = ~Dim1, y = ~Dim2, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

p <- plot_ly(pc, x = ~Dim1, y = ~Dim3, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim3')))

p

p <- plot_ly(pc, x = ~Dim2, y = ~Dim4, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim4')))

p

correlation ~X3D features

####################################################################################################################
#Elegimos las componentes a utilizar
num.cols <- names(which(sapply(feat_X3D_train, function(x) is.numeric(x) )))

svd_seq<-feat_X3D_train[,num.cols ]
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)

PCA from ~X3D features


# conduct PCA on training dataset
num.cols <- names(which(sapply(feat_X3D_train, function(x) is.numeric(x) )))

pca <- prcomp(feat_X3D_train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                                            geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~X3D features ")
ggplotly(p)
#Replace features by PCA components
feat_X3D_train_pca<-as.data.frame(pca$x[,1:10])
colnames(feat_X3D_train_pca)<-paste("pca_X3D_",1:10,sep="")

### Apply to testing
feat_X3D_test_pca<-as.data.frame(predict(pca, newdata=feat_X3D_test[,num.cols])[,1:10])
colnames(feat_X3D_test_pca)<-paste("pca_X3D_",1:10,sep="")

scatter first 4 dimensions


library(plotly)

pc<-as.data.frame(pca$x[,1:4])%>%
                      rename(Dim1=PC1,
                             Dim2=PC2,
                             Dim3=PC3,
                             Dim4=PC4)

p <- plot_ly(pc, x = ~Dim1, y = ~Dim2, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

p <- plot_ly(pc, x = ~Dim1, y = ~Dim3, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim3')))

p

p <- plot_ly(pc, x = ~Dim2, y = ~Dim4, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim4')))

p

Análisis de correlación

####################################################################################################################
#Elegimos las componentes a utilizar
num.cols <- names(which(sapply(feat_rest_train, function(x) is.numeric(x) )))

svd_seq<-feat_rest_train[,num.cols ]
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)

PCA from ~rest features


# conduct PCA on training dataset
num.cols <- names(which(sapply(feat_rest_train, function(x) is.numeric(x) )))

pca <- prcomp(feat_rest_train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                                            geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~X3D features ")
ggplotly(p)
#Replace features by PCA components
feat_rest_train_pca<-as.data.frame(pca$x[,1:12])
colnames(feat_rest_train_pca)<-paste("pca_rest_",1:12,sep="")

## Apply to testing
feat_rest_test_pca<-as.data.frame(predict(pca, newdata=feat_rest_test[,num.cols])[,1:12])
colnames(feat_rest_test_pca)<-paste("pca_rest_",1:12,sep="")

scatter first 4 dimensions


library(plotly)

pc<-as.data.frame(pca$x[,1:4])%>%
                      rename(Dim1=PC1,
                             Dim2=PC2,
                             Dim3=PC3,
                             Dim4=PC4)

p <- plot_ly(pc, x = ~Dim1, y = ~Dim2, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

p <- plot_ly(pc, x = ~Dim1, y = ~Dim3, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim3')))

p

p <- plot_ly(pc, x = ~Dim2, y = ~Dim4, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim4')))

p

Análisis de correlación X3D + PCA

testcor<-cbind(feat_X3D_train_pca, feat_X3D_train)

svd_seq<-testcor
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)


#feat_X3D_train_pca, feat_X3D_train, feat_pixel_train_pca,feat_rest_train_pca
---
title: "Técnicas de  clustering aplicado al estudio de variantes de genomas virales"
output:
  html_document: default
  html_notebook:
    code_folding: hide
    fig_height: 6
    fig_width: 10
---



```{r  message=FALSE, warning=FALSE, setup}
#librerias necesarias


#library (Biostrings) # Manipulacion de secuencias y alineamientos

library(ggplot2)
library(plotly)
library (RColorBrewer)
library(caret)
library(tidyr)
library(dplyr)
#library(reshape2)
library(psych)
library(irlba)
library(fpc)

```



### Data
```{r warning= FALSE, message=FALSE }
train_v1<-read.csv("ccs.new_train_v1.csv", stringsAsFactors = FALSE)%>%select(-id)
trainCat<-train_v1%>%select(type_cat, image_name)
test_v1<-read.csv("ccs.new_test_v1.csv" , stringsAsFactors = FALSE)%>%select(-id)

```



### NSFW
```{r warning= FALSE, message=FALSE }
NSFWtrain<-read.csv("nsfwOut_train.csv" , stringsAsFactors = FALSE, header=FALSE)%>%
                rename(image_name=V1, nsfw_val=V2)%>%
                    mutate(nsfw_val_log=log(nsfw_val))
NSFWtest<-read.csv("nsfwOut_test.csv" , stringsAsFactors = FALSE, header=FALSE)%>%
  
                rename(image_name=V1, nsfw_val=V2)%>%
                    mutate(nsfw_val_log=log(nsfw_val))




NSFWtrain<-left_join(trainCat,NSFWtrain, by=c("image_name"="image_name"))

NSFWtrain%>%
  spread(type_cat, nsfw_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~Type_1, name="Type_1") %>%
          add_histogram(x = ~Type_2,name="Type_2" ) %>%
           add_histogram(x = ~Type_3, name="Type_3" ) %>%
        layout(barmode = "overlay",title = "NSFW value")

## Logaritmic
NSFWtrain%>%
  spread(type_cat, nsfw_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(Type_1), name="Type_1") %>%
          add_histogram(x = ~log(Type_2),name="Type_2" ) %>%
           add_histogram(x = ~log(Type_3), name="Type_3" ) %>%
        layout(barmode = "overlay", title = "log(NSFW value)")


##Check consistency at both sets
head(cbind(train.mean = lapply(NSFWtrain%>%select(nsfw_val,nsfw_val_log),
                               function(x){mean(x,na.rm=TRUE)} ),
              train.sd = lapply(NSFWtrain%>%select(nsfw_val,nsfw_val_log),
                            function(x){sd(x,na.rm=TRUE)} ),
                 test.mean = lapply(NSFWtest%>%select(nsfw_val,nsfw_val_log),
                                   function(x){mean(x,na.rm=TRUE)}),
                      test.sd = lapply(NSFWtest%>%select(nsfw_val,nsfw_val_log),
                                       function(x){sd(x,na.rm=TRUE)})))



```




### Distance
```{r warning= FALSE, message=FALSE }
Distrain<-read.csv("train_distance.csv" , stringsAsFactors = FALSE,
                   header=FALSE)%>%
                     rename(image_name=V1, dist_val=V2)%>%
                        mutate(dist_val_log=log(dist_val))
  
Distest<-read.csv("test_distance.csv" , stringsAsFactors = FALSE,
                  header=FALSE)%>%
                      rename(image_name=V1, dist_val=V2)%>%
                          mutate(dist_val_log=log(dist_val))

Distrain<-left_join(trainCat,Distrain, by=c("image_name"="image_name"))


##Check consistency at both sets
head(cbind(train.mean = lapply(Distrain%>%select(dist_val,dist_val_log),
                               function(x){mean(x,na.rm=TRUE)} ),
              train.sd = lapply(Distrain%>%select(dist_val,dist_val_log),
                            function(x){sd(x,na.rm=TRUE)} ),
                 test.mean = lapply(Distest%>%select(dist_val,dist_val_log),
                                   function(x){mean(x,na.rm=TRUE)}),
                      test.sd = lapply(Distest%>%select(dist_val,dist_val_log),
                                       function(x){sd(x,na.rm=TRUE)})))



#scale train
Distrain_s<- scale(as.matrix(Distrain%>%select(dist_val, dist_val_log)),
                   center=TRUE,scale=TRUE)

#scale testing
Distest_s<- scale(as.matrix(Distest%>%select(dist_val, dist_val_log),
                                attr(Distrain_s, "scaled:center"),
                                    attr(Distrain_s, "scaled:scale") ) )


##Transform both sets
Distrain<-cbind(Distrain%>%select(type_cat, image_name) , Distrain_s[,])
Distest<-cbind(Distest%>%select( image_name) , Distest_s[,])




Distrain%>%
  spread(type_cat, dist_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~Type_1, name="Type_1") %>%
          add_histogram(x = ~Type_2,name="Type_2" ) %>%
           add_histogram(x = ~Type_3, name="Type_3" ) %>%
        layout(barmode = "overlay",
               title = "Distance")

### Logaritm
Distrain%>%
  spread(type_cat, dist_val)%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(Type_1), name="Type_1") %>%
          add_histogram(x = ~log(Type_2),name="Type_2" ) %>%
           add_histogram(x = ~log(Type_3), name="Type_3" ) %>%
        layout(barmode = "overlay",
               title = "Log (Distance)")


## Check
Distrain[1:5,1:4]
Distest[1:5,1:3]

##Check consistency at both sets
head(cbind(train.mean = lapply(Distrain%>%select(dist_val,dist_val_log),
                               function(x){mean(x,na.rm=TRUE)} ),
              train.sd = lapply(Distrain%>%select(dist_val,dist_val_log),
                            function(x){sd(x,na.rm=TRUE)} ),
                 test.mean = lapply(Distest%>%select(dist_val,dist_val_log),
                                   function(x){mean(x,na.rm=TRUE)}),
                      test.sd = lapply(Distest%>%select(dist_val,dist_val_log),
                                       function(x){sd(x,na.rm=TRUE)})))


```






### hashing
```{r warning= FALSE, message=FALSE }
pHashtrain<-read.csv("hashes_16_phash_training.csv" , stringsAsFactors = FALSE, header=FALSE)%>%select(-V2)%>%
          #mutate_each(funs(whatever = ./15), V3:V66)
            mutate_each(funs(./15), V3:V66)
pHashtest<-read.csv("hashes_16_phash_testing.csv" , stringsAsFactors = FALSE, header=FALSE)%>%select(-V2)%>%
          mutate_each(funs(./15), V3:V66)



### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(pHashtrain)
nzv_12<- nearZeroVar(pHashtest)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        pHashtrain<- pHashtrain[, -unique(c(nzv_11,nzv_12))]
        pHashtest<- pHashtest[, -unique(c(nzv_11,nzv_12))]
        }

##Check consistency at both sets
head(cbind(train.mean = lapply(pHashtrain[,-1], mean),
          train.sd = lapply(pHashtrain[,-1], sd),
                test.mean = lapply(pHashtest[,-1], mean),
                      test.sd = lapply(pHashtest[,-1], sd)))



# conduct PCA on training dataset
num.cols <- names(which(sapply( pHashtrain, function(x) is.numeric(x) )))

pca <- prcomp(pHashtrain[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)



### Plot importance

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~pixel features ")
ggplotly(p)





#Replace features by PCA components
pHashtrain_pca<-as.data.frame(pca$x[,1:40])
colnames(pHashtrain_pca)<-paste("pca_pHash_",1:40,sep="")

### APPLY TO TESTING DATA 
pHashtest_pca<-as.data.frame(predict(pca, newdata=pHashtest[,num.cols])[,1:40])
colnames(pHashtest_pca)<-paste("pca_pHash_",1:40,sep="")


pHashtrain<-cbind(pHashtrain[,1],pHashtrain_pca)

pHashtest<-cbind(pHashtest[,1],pHashtest_pca)
colnames(pHashtrain)[1]<-colnames(pHashtest)[1]<-"image_name"
pHashtrain<-left_join(trainCat,pHashtrain, by=c("image_name"="image_name"))


#### Plot Four first dimensions
p <- plot_ly(pHashtrain, x = ~pca_pHash_1, y = ~pca_pHash_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

pHashtrain[1:5,1:5]
pHashtest[1:5,1:5]

```



### Networks
```{r warning= FALSE, message=FALSE }
Namestrain<-read.csv("Names_train_features.csv" , stringsAsFactors = FALSE)
Namestest<-read.csv("Names_test_features.csv" , stringsAsFactors = FALSE)

##### Inceptionv3
Inceptrain<- cbind(Namestrain,read.csv("Inception3_train_features.csv", stringsAsFactors = FALSE) )
Inceptest<- cbind(Namestest, read.csv("Inception3_test_features.csv", stringsAsFactors = FALSE) )

##### ResNet50
RN50train<-cbind(Namestrain, read.csv("ResNet50_train_features.csv", stringsAsFactors = FALSE) )
RN50test<-cbind(Namestest ,read.csv("ResNet50_test_features.csv", stringsAsFactors = FALSE) )

#####VGG19 
VGG19train<-cbind(Namestrain ,read.csv("VGG19_train_features.csv" , stringsAsFactors = FALSE) )
VGG19test<-cbind(Namestest, read.csv("VGG19_test_features.csv" , stringsAsFactors = FALSE) )

```





### InceptionV3 Net
```{r warning= FALSE, message=FALSE }


### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(Inceptrain)
nzv_12<- nearZeroVar(Inceptest)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        Inceptrain<- Inceptrain[, -unique(c(nzv_11,nzv_12))]
        Inceptrain<- Inceptest[, -unique(c(nzv_11,nzv_12))]
        }

##Check consistency at both sets
head(cbind(train.mean = lapply(Inceptrain[,-1], mean),
          train.sd = lapply(Inceptrain[,-1], sd),
                test.mean = lapply(Inceptest[,-1], mean),
                      test.sd = lapply(Inceptest[,-1], sd)))



#### Plot first
Inceptrain%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~X0, name="X0") %>%
          add_histogram(x = ~X1,name="X1" ) %>%
           add_histogram(x = ~X2, name="X2" ) %>%
              add_histogram(x = ~X3, name="X3" ) %>%
                add_histogram(x = ~X4, name="X4" ) %>%
                    add_histogram(x = ~X5, name="X5" ) %>%
                      add_histogram(x = ~X6, name="X6" ) %>%
                           add_histogram(x = ~X7, name="X7" ) %>%
                                add_histogram(x = ~X8, name="X8" ) %>%
                                     add_histogram(x = ~X9, name="X9" ) %>%
                                        add_histogram(x = ~X10, name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "Inceptrain_outputs")


#### Plot log
Inceptrain%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(X0), name="X0") %>%
          add_histogram(x = ~log(X1),name="X1" ) %>%
           add_histogram(x = ~log(X2), name="X2" ) %>%
              add_histogram(x = ~log(X3), name="X3" ) %>%
                add_histogram(x = ~log(X4), name="X4" ) %>%
                    add_histogram(x = ~log(X5), name="X5" ) %>%
                      add_histogram(x = ~log(X6), name="X6" ) %>%
                           add_histogram(x = ~log(X7), name="X7" ) %>%
                                add_histogram(x = ~log(X8), name="X8" ) %>%
                                     add_histogram(x = ~log(X9), name="X9" ) %>%
                                        add_histogram(x = ~log(X10), name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "log(Inceptrain_outputs)")


####################################################################################################
####################################################################################################

# conduct PCA on training dataset
num.cols <- names(which(sapply( Inceptrain, function(x) is.numeric(x) )))

pca <- prcomp(Inceptrain[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)



### Plot importance

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~Inceptrain features ")
ggplotly(p)

####################################################################################################
####################################################################################################
####################################################################################################



#Replace features by PCA components
Inceptrain_pca<-as.data.frame(pca$x[,1:50])
colnames(Inceptrain_pca)<-paste("pca_Incep3_",1:50,sep="")

### APPLY TO TESTING DATA 
Inceptest_pca<-as.data.frame(predict(pca, newdata=Inceptest[,num.cols])[,1:50])
colnames(Inceptest_pca)<-paste("pca_Incep3_",1:50,sep="")


Inceptrain<-cbind(Inceptrain[,1],Inceptrain_pca)
Inceptest<-cbind(Inceptest[,1],Inceptest_pca)

colnames(Inceptrain)[1]<-colnames(Inceptest)[1]<-"image_name"
Inceptrain<-left_join(trainCat,Inceptrain, by=c("image_name"="image_name"))


#### Plot Four first dimensions
p <- plot_ly(Inceptrain, x = ~pca_Incep3_1, y = ~pca_Incep3_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p



#### Plot first
Inceptrain%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~pca_Incep3_1, name="cmp1") %>%
          add_histogram(x = ~pca_Incep3_2,name="cmp2" ) %>%
           add_histogram(x = ~pca_Incep3_3, name="cmp3" ) %>%
              add_histogram(x = ~pca_Incep3_4, name="cmp4" ) %>%
                add_histogram(x = ~pca_Incep3_5, name="cmp5" ) %>%
                    add_histogram(x = ~pca_Incep3_6, name="cmp6" ) %>%
                      add_histogram(x = ~pca_Incep3_7, name="cmp7" ) %>%
                           add_histogram(x = ~pca_Incep3_8, name="cmp8" ) %>%
                                add_histogram(x = ~pca_Incep3_9, name="cmp9" ) %>%
                                     add_histogram(x = ~pca_Incep3_10, name="cmp10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "Incep3_outputs")


Inceptrain[1:5,1:5]
Inceptest[1:5,1:5]

```










### ResNet50 Net
```{r warning= FALSE, message=FALSE }


### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(RN50train)
nzv_12<- nearZeroVar(RN50test)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        RN50train<- RN50train[, -unique(c(nzv_11,nzv_12))]
        RN50train<- RN50test[, -unique(c(nzv_11,nzv_12))]
        }

##Check consistency at both sets
head(cbind(train.mean = lapply(RN50train[,-1], mean),
          train.sd = lapply(RN50train[,-1], sd),
                test.mean = lapply(RN50test[,-1], mean),
                      test.sd = lapply(RN50test[,-1], sd)))



#### Plot first
RN50train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~X0, name="X0") %>%
          add_histogram(x = ~X1,name="X1" ) %>%
           add_histogram(x = ~X2, name="X2" ) %>%
              add_histogram(x = ~X3, name="X3" ) %>%
                add_histogram(x = ~X4, name="X4" ) %>%
                    add_histogram(x = ~X5, name="X5" ) %>%
                      add_histogram(x = ~X6, name="X6" ) %>%
                           add_histogram(x = ~X7, name="X7" ) %>%
                                add_histogram(x = ~X8, name="X8" ) %>%
                                     add_histogram(x = ~X9, name="X9" ) %>%
                                        add_histogram(x = ~X10, name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "RN50train_outputs")


#### Plot log
RN50train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(X0), name="X0") %>%
          add_histogram(x = ~log(X1),name="X1" ) %>%
           add_histogram(x = ~log(X2), name="X2" ) %>%
              add_histogram(x = ~log(X3), name="X3" ) %>%
                add_histogram(x = ~log(X4), name="X4" ) %>%
                    add_histogram(x = ~log(X5), name="X5" ) %>%
                      add_histogram(x = ~log(X6), name="X6" ) %>%
                           add_histogram(x = ~log(X7), name="X7" ) %>%
                                add_histogram(x = ~log(X8), name="X8" ) %>%
                                     add_histogram(x = ~log(X9), name="X9" ) %>%
                                        add_histogram(x = ~log(X10), name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "log(RN50train_outputs)")


####################################################################################################
####################################################################################################

# conduct PCA on training dataset
num.cols <- names(which(sapply( RN50train, function(x) is.numeric(x) )))

pca <- prcomp(RN50train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)



### Plot importance

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~RN50train features ")
ggplotly(p)

####################################################################################################
####################################################################################################
####################################################################################################



#Replace features by PCA components
RN50train_pca<-as.data.frame(pca$x[,1:40])
colnames(RN50train_pca)<-paste("pca_RN50_",1:40,sep="")

### APPLY TO TESTING DATA 
RN50test_pca<-as.data.frame(predict(pca, newdata=RN50test[,num.cols])[,1:40])
colnames(RN50test_pca)<-paste("pca_RN50_",1:40,sep="")


RN50train<-cbind(RN50train[,1],RN50train_pca)
RN50test<-cbind(RN50test[,1],RN50test_pca)

colnames(RN50train)[1]<-colnames(RN50test)[1]<-"image_name"
RN50train<-left_join(trainCat,RN50train, by=c("image_name"="image_name"))


#### Plot Four first dimensions
p <- plot_ly(RN50train, x = ~pca_RN50_1, y = ~pca_RN50_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p



#### Plot first
RN50train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~pca_RN50_1, name="cmp1") %>%
          add_histogram(x = ~pca_RN50_2,name="cmp2" ) %>%
           add_histogram(x = ~pca_RN50_3, name="cmp3" ) %>%
              add_histogram(x = ~pca_RN50_4, name="cmp4" ) %>%
                add_histogram(x = ~pca_RN50_5, name="cmp5" ) %>%
                    add_histogram(x = ~pca_RN50_6, name="cmp6" ) %>%
                      add_histogram(x = ~pca_RN50_7, name="cmp7" ) %>%
                           add_histogram(x = ~pca_RN50_8, name="cmp8" ) %>%
                                add_histogram(x = ~pca_RN50_9, name="cmp9" ) %>%
                                     add_histogram(x = ~pca_RN50_10, name="cmp10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "RN50_outputs")


RN50train[1:5,1:5]
RN50test[1:5,1:5]
```









### VGG19 Net
```{r warning= FALSE, message=FALSE }


### REMOVE NEAR ZERO VARIATION
nzv_11<- nearZeroVar(VGG19train)
nzv_12<- nearZeroVar(VGG19test)
if(length(unique(c(nzv_11,nzv_12)))>0)
      {
        VGG19train<- VGG19train[, -unique(c(nzv_11,nzv_12))]
        VGG19train<- VGG19test[, -unique(c(nzv_11,nzv_12))]
        }

##Check consistency at both sets
head(cbind(train.mean = lapply(VGG19train[,-1], mean),
          train.sd = lapply(VGG19train[,-1], sd),
                test.mean = lapply(VGG19test[,-1], mean),
                      test.sd = lapply(VGG19test[,-1], sd)))



#### Plot first
VGG19train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~X0, name="X0") %>%
          add_histogram(x = ~X1,name="X1" ) %>%
           add_histogram(x = ~X2, name="X2" ) %>%
              add_histogram(x = ~X3, name="X3" ) %>%
                add_histogram(x = ~X4, name="X4" ) %>%
                    add_histogram(x = ~X5, name="X5" ) %>%
                      add_histogram(x = ~X6, name="X6" ) %>%
                           add_histogram(x = ~X7, name="X7" ) %>%
                                add_histogram(x = ~X8, name="X8" ) %>%
                                     add_histogram(x = ~X9, name="X9" ) %>%
                                        add_histogram(x = ~X10, name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "VGG19train_outputs")


#### Plot log
VGG19train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~log(X0), name="X0") %>%
          add_histogram(x = ~log(X1),name="X1" ) %>%
           add_histogram(x = ~log(X2), name="X2" ) %>%
              add_histogram(x = ~log(X3), name="X3" ) %>%
                add_histogram(x = ~log(X4), name="X4" ) %>%
                    add_histogram(x = ~log(X5), name="X5" ) %>%
                      add_histogram(x = ~log(X6), name="X6" ) %>%
                           add_histogram(x = ~log(X7), name="X7" ) %>%
                                add_histogram(x = ~log(X8), name="X8" ) %>%
                                     add_histogram(x = ~log(X9), name="X9" ) %>%
                                        add_histogram(x = ~log(X10), name="X10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "log(VGG19train_outputs)")


####################################################################################################
####################################################################################################

# conduct PCA on training dataset
num.cols <- names(which(sapply( VGG19train, function(x) is.numeric(x) )))

pca <- prcomp(VGG19train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)



### Plot importance

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~VGG19train features ")
ggplotly(p)

####################################################################################################
####################################################################################################
####################################################################################################



#Replace features by PCA components
VGG19train_pca<-as.data.frame(pca$x[,1:40])
colnames(VGG19train_pca)<-paste("pca_VGG19_",1:40,sep="")

### APPLY TO TESTING DATA 
VGG19test_pca<-as.data.frame(predict(pca, newdata=VGG19test[,num.cols])[,1:40])
colnames(VGG19test_pca)<-paste("pca_VGG19_",1:40,sep="")


VGG19train<-cbind(VGG19train[,1],VGG19train_pca)
VGG19test<-cbind(VGG19test[,1],VGG19test_pca)

colnames(VGG19train)[1]<-colnames(VGG19test)[1]<-"image_name"
VGG19train<-left_join(trainCat,VGG19train, by=c("image_name"="image_name"))


#### Plot Four first dimensions
p <- plot_ly(VGG19train, x = ~pca_VGG19_1, y = ~pca_VGG19_2, color = ~as.factor(type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p



#### Plot first
VGG19train%>%
    plot_ly(alpha = 0.6) %>%
        add_histogram(x = ~pca_VGG19_1, name="cmp1") %>%
          add_histogram(x = ~pca_VGG19_2,name="cmp2" ) %>%
           add_histogram(x = ~pca_VGG19_3, name="cmp3" ) %>%
              add_histogram(x = ~pca_VGG19_4, name="cmp4" ) %>%
                add_histogram(x = ~pca_VGG19_5, name="cmp5" ) %>%
                    add_histogram(x = ~pca_VGG19_6, name="cmp6" ) %>%
                      add_histogram(x = ~pca_VGG19_7, name="cmp7" ) %>%
                           add_histogram(x = ~pca_VGG19_8, name="cmp8" ) %>%
                                add_histogram(x = ~pca_VGG19_9, name="cmp9" ) %>%
                                     add_histogram(x = ~pca_VGG19_10, name="cmp10" ) %>%
                                        layout(barmode = "overlay",
                                               title = "VGG19_outputs")


VGG19train[1:5,1:5]
VGG19test[1:5,1:5]
```



#Generate outputs
```{r warning= FALSE, message=FALSE ,fig.align = 'center' }

train_v2<-left_join(NSFWtrain, Distrain%>%select(-type_cat), by=c("image_name"="image_name") )
train_v2<-left_join(train_v2, pHashtrain%>%select(-type_cat), by=c("image_name"="image_name") )
train_v2<-left_join(train_v2, Inceptrain%>%select(-type_cat), by=c("image_name"="image_name") )
train_v2<-left_join(train_v2, RN50train%>%select(-type_cat), by=c("image_name"="image_name") )
train_v2<-left_join(train_v2, VGG19train%>%select(-type_cat), by=c("image_name"="image_name") )

library(xda)
xdatrain<-numSummary(train_v2)
train_v2[is.na(train_v2)] <- 0
xdatrain<-numSummary(train_v2)

test_v2<-left_join(NSFWtest, Distest, by=c("image_name"="image_name" ) )
test_v2<-left_join(test_v2, pHashtest, by=c("image_name"="image_name") )
test_v2<-left_join(test_v2, Inceptest, by=c("image_name"="image_name") )
test_v2<-left_join(test_v2, RN50test, by=c("image_name"="image_name") )
test_v2<-left_join(test_v2, VGG19test, by=c("image_name"="image_name") )

xdatest<-numSummary(test_v2)
test_v2[is.na(test_v2)] <- 0
xdatest<-numSummary(test_v2)

train_v3<-left_join(train_v1, train_v2%>%select(-type_cat), by=c("image_name"="image_name") )
test_v3<-left_join(test_v1, test_v2, by=c("image_name"="image_name" ) )

write.csv(train_v1,"train_v1.csv",row.names = FALSE )
write.csv(test_v1,"test_v1.csv",row.names = FALSE )

write.csv(train_v2,"train_v2.csv",row.names = FALSE )
write.csv(test_v2,"test_v2.csv",row.names = FALSE )

write.csv(train_v3,"train_v3.csv",row.names = FALSE )
write.csv(test_v3,"test_v3.csv",row.names = FALSE )
```






### correlation  ~rest features
```{r warning= FALSE, message=FALSE ,fig.align = 'center' }

#scale train
feat_rest_train_s<- scale(as.matrix(feat_rest_train%>%select(-creation_time))
                                                                  , center=TRUE,scale=TRUE)

#scale testing
feat_rest_test_s<- scale(as.matrix(feat_rest_test%>%select(-creation_time)
                                                                  , center=TRUE,scale=TRUE),
                                                                     attr(feat_rest_train_s, "scaled:center"),
                                                                        attr(feat_rest_train_s, "scaled:scale") )


##Transform both sets
feat_rest_train<-as.data.frame(feat_rest_train_s[,])
feat_rest_test<-as.data.frame(feat_rest_test_s[,])

## Check
feat_rest_train[1:5,1:5]
feat_rest_test[1:5,1:5]

### Scale alt
#feat_rest_test<- scale(as.matrix(feat_rest_test), center = mean(feat_rest_train), scale = sd(feat_rest_train) ))



# #to data frame
# feat_rest_train_s<-as.data.frame(feat_rest_train_s)
#     feat_rest_test_s<-as.data.frame(feat_rest_test_s)
# 
# 
# ##############################################3
# rep_rest<-cbind(train.mean = lapply(feat_rest_train_s[,-1], mean),
#                      train.sd = lapply(feat_rest_train_s[,-1], sd),
#                            test.mean = lapply(feat_rest_test_s[,-1], mean),
#                                 test.sd = lapply(feat_rest_test_s[,-1], sd))
# head(rep_rest)


##############################################3

# 
# ### check in detail
# library(xda)
# xdatrain<-numSummary(feat_rest_train_s)
# head(xdatrain)
# xdatest<-numSummary(feat_rest_test_s)
# head(xdatest)
# #xdatab2<-charSummary(tab3)
```

```{r warning= FALSE, message=FALSE ,fig.align = 'center' }


#scale train
feat_X3D_train_s<- scale(as.matrix(feat_X3D_train), center=TRUE,scale=TRUE)

#scale testing
feat_X3D_test_s<- scale(as.matrix(feat_X3D_test),
                         attr(feat_X3D_train_s, "scaled:center"),
                                        attr(feat_X3D_train_s, "scaled:scale") )


##Transform both sets
feat_X3D_train<-as.data.frame(feat_X3D_train_s[,])
feat_X3D_test<-as.data.frame(feat_X3D_test_s[,])

## Check
feat_X3D_train[1:5,1:5]
feat_X3D_test[1:5,1:5]
# 
# #to data frame
# feat_X3D_train_s<-as.data.frame(feat_X3D_train_s)
#     feat_X3D_test_s<-as.data.frame(feat_X3D_test_s)
# 
# 
# ##############################################3
# rep_X3D<-cbind(train.mean = lapply(feat_X3D_train_s[,-1], mean),
#                      train.sd = lapply(feat_X3D_train_s[,-1], sd),
#                            test.mean = lapply(feat_X3D_test_s[,-1], mean),
#                                 test.sd = lapply(feat_X3D_test_s[,-1], sd))
# head(rep_X3D)
# 
# 
# ##############################################3
# 
# 
# ### check in detail
# library(xda)
# xdatrain<-numSummary(feat_X3D_train_s)
# head(xdatrain)
# xdatest<-numSummary(feat_X3D_test_s)
# head(xdatest)
#xdatab2<-cha
```


```{r warning= FALSE, message=FALSE ,fig.align = 'center' }

#scale train
feat_pixel_train_s<- scale(as.matrix(feat_pixel_train), center=TRUE,scale=TRUE)

#scale testing
feat_pixel_test_s<- scale(as.matrix(feat_pixel_test),
                         attr(feat_pixel_train_s, "scaled:center"),
                                        attr(feat_pixel_train_s, "scaled:scale") )

##Transform both sets
feat_pixel_train<-as.data.frame(feat_pixel_train_s[,])
feat_pixel_test<-as.data.frame(feat_pixel_test_s[,])

## Check
feat_pixel_train[1:5,1:5]
feat_pixel_test[1:5,1:5]

# 
# 
# 
# #to data frame
# feat_pixel_train_s<-as.data.frame(feat_pixel_train_s)
#     feat_pixel_test_s<-as.data.frame(feat_pixel_test_s)
# 
# 
# ##############################################3
# rep_pixel<-cbind(train.mean = lapply(feat_pixel_train_s[,-1], mean),
#                      train.sd = lapply(feat_pixel_train_s[,-1], sd),
#                            test.mean = lapply(feat_pixel_test_s[,-1], mean),
#                                 test.sd = lapply(feat_pixel_test_s[,-1], sd))
# head(rep_pixel)
# 
# 
# ##############################################3
# 
# 
# ### check in detail
# library(xda)
# xdatrain<-numSummary(feat_pixel_train_s)
# head(xdatrain)
# xdatest<-numSummary(feat_pixel_test_s)
# head(xdatest)
# #xdatab2<-cha
```



### correlation  ~pixel features
```{r warning= FALSE, message=FALSE ,fig.align = 'center' }
####################################################################################################################
#Elegimos las componentes a utilizar
num.cols <- names(which(sapply(feat_pixel_train, function(x) is.numeric(x) )))

set.seed(1234)
num.cols<- sample(num.cols, 100)

svd_seq<-feat_pixel_train[,num.cols ]
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)
```

### PCA from ~pixel features
```{r warning= FALSE, message=FALSE }

# conduct PCA on training dataset
num.cols <- names(which(sapply(feat_pixel_train, function(x) is.numeric(x) )))

pca <- prcomp(feat_pixel_train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                    geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~pixel features ")
ggplotly(p)
#Replace features by PCA components
feat_pixel_train_pca<-as.data.frame(pca$x[,1:100])
colnames(feat_pixel_train_pca)<-paste("pca_pixel_",1:100,sep="")

### APPLY TO TESTING DATA 
feat_pixel_test_pca<-as.data.frame(predict(pca, newdata=feat_pixel_test[,num.cols])[,1:100])
colnames(feat_pixel_test_pca)<-paste("pca_pixel_",1:100,sep="")


```


### scatter first 4 dimensions
```{r warning= FALSE, message=FALSE ,fig.width = 10, fig.height = 10 , fig.align = 'center' }

library(plotly)

pc<-as.data.frame(pca$x[,1:4])%>%
                      rename(Dim1=PC1,
                             Dim2=PC2,
                             Dim3=PC3,
                             Dim4=PC4)

p <- plot_ly(pc, x = ~Dim1, y = ~Dim2, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

p <- plot_ly(pc, x = ~Dim1, y = ~Dim3, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim3')))

p

p <- plot_ly(pc, x = ~Dim2, y = ~Dim4, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim4')))

p


```

### correlation  ~X3D features
```{r warning= FALSE, message=FALSE ,fig.align = 'center' }
####################################################################################################################
#Elegimos las componentes a utilizar
num.cols <- names(which(sapply(feat_X3D_train, function(x) is.numeric(x) )))

svd_seq<-feat_X3D_train[,num.cols ]
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)
```


### PCA from ~X3D features
```{r warning= FALSE, message=FALSE }

# conduct PCA on training dataset
num.cols <- names(which(sapply(feat_X3D_train, function(x) is.numeric(x) )))

pca <- prcomp(feat_X3D_train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                                            geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~X3D features ")
ggplotly(p)
#Replace features by PCA components
feat_X3D_train_pca<-as.data.frame(pca$x[,1:10])
colnames(feat_X3D_train_pca)<-paste("pca_X3D_",1:10,sep="")

### Apply to testing
feat_X3D_test_pca<-as.data.frame(predict(pca, newdata=feat_X3D_test[,num.cols])[,1:10])
colnames(feat_X3D_test_pca)<-paste("pca_X3D_",1:10,sep="")
```

### scatter first 4 dimensions
```{r warning= FALSE, message=FALSE ,fig.width = 10, fig.height = 10 , fig.align = 'center' }

library(plotly)

pc<-as.data.frame(pca$x[,1:4])%>%
                      rename(Dim1=PC1,
                             Dim2=PC2,
                             Dim3=PC3,
                             Dim4=PC4)

p <- plot_ly(pc, x = ~Dim1, y = ~Dim2, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

p <- plot_ly(pc, x = ~Dim1, y = ~Dim3, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim3')))

p

p <- plot_ly(pc, x = ~Dim2, y = ~Dim4, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim4')))

p


```


### Análisis de correlación
```{r warning= FALSE, message=FALSE ,fig.align = 'center' }
####################################################################################################################
#Elegimos las componentes a utilizar
num.cols <- names(which(sapply(feat_rest_train, function(x) is.numeric(x) )))

svd_seq<-feat_rest_train[,num.cols ]
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)
```


### PCA from ~rest features
```{r warning= FALSE, message=FALSE }

# conduct PCA on training dataset
num.cols <- names(which(sapply(feat_rest_train, function(x) is.numeric(x) )))

pca <- prcomp(feat_rest_train[,num.cols], retx=TRUE, center=FALSE, scale=FALSE)
#Eigenvalues
eig <- (pca$sdev)^2 ; variance <-eig*100/sum(eig) ; cumvar <- cumsum(variance) ; Comp<- 1:length(cumvar)

p<-as.data.frame(cbind(Comp,cumvar)) %>%
                ggplot(aes(x =factor(Comp), y = cumvar)) + 
                                            geom_bar(stat = "identity")  + geom_hline(yintercept=95, col="red") +
                            xlab("components  ~X3D features ")
ggplotly(p)
#Replace features by PCA components
feat_rest_train_pca<-as.data.frame(pca$x[,1:12])
colnames(feat_rest_train_pca)<-paste("pca_rest_",1:12,sep="")

## Apply to testing
feat_rest_test_pca<-as.data.frame(predict(pca, newdata=feat_rest_test[,num.cols])[,1:12])
colnames(feat_rest_test_pca)<-paste("pca_rest_",1:12,sep="")
```


### scatter first 4 dimensions
```{r warning= FALSE, message=FALSE ,fig.width = 10, fig.height = 10 , fig.align = 'center' }

library(plotly)

pc<-as.data.frame(pca$x[,1:4])%>%
                      rename(Dim1=PC1,
                             Dim2=PC2,
                             Dim3=PC3,
                             Dim4=PC4)

p <- plot_ly(pc, x = ~Dim1, y = ~Dim2, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2')))

p

p <- plot_ly(pc, x = ~Dim1, y = ~Dim3, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim3')))

p

p <- plot_ly(pc, x = ~Dim2, y = ~Dim4, color = ~as.factor(train_index$type_cat)) %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim4')))

p



```




### Análisis de correlación X3D + PCA
```{r warning= FALSE, message=FALSE ,fig.align = 'center' }
testcor<-cbind(feat_X3D_train_pca, feat_X3D_train)

svd_seq<-testcor
# Correlation scatter plots for all combinations between the first four principal components.
library(reshape2)
cormat <- round(cor(svd_seq),4)
melted_cormat <- melt(cormat)
head(melted_cormat)


p<-ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile()
ggplotly(p)


#feat_X3D_train_pca, feat_X3D_train, feat_pixel_train_pca,feat_rest_train_pca

```



